A novel approach to intrusion detection system using hybrid flower pollination and cheetah optimization algorithm.

Area of Science:

• Cybersecurity
• Artificial Intelligence
• Network Intrusion Detection

Background:

• Traditional intrusion detection systems (IDS) struggle with adaptability, precision, and high false positive rates in dynamic network environments.
• Limitations in current IDS hinder effective real-time threat detection in complex network traffic patterns.

Purpose of the Study:

• To develop a novel hybrid IDS model integrating Flower Pollination Algorithm (FPA), Cheetah Optimization Algorithm (COA), and Artificial Neural Networks (ANN).
• To enhance detection accuracy, reduce false positives, and optimize feature selection, anomaly detection, and rule adaptation in network security.
• To address the critical challenges faced by traditional IDS in dynamic network environments.

Main Methods:

• Proposed a hybrid FPA-COA-ANN model combining optimization algorithms (FPA, COA) with Artificial Neural Networks (ANN).
• Evaluated the model's performance on five benchmark datasets: CICIDS-2017, TII-SSRC, Lu-flow, NSL-KDD, and WSN-DS.
• Analyzed key performance metrics to assess effectiveness in detecting malicious activities.

Main Results:

• Achieved high accuracy rates: 0.99 (CICIDS-2017), 1.00 (TII-SSRC), 1.00 (Lu-flow), 0.99 (NSL-KDD), and 0.93 (WSN-DS).
• Demonstrated superior performance compared to existing IDS approaches, with significant improvements in detection precision and adaptability.
• Showcased a notable reduction in false positive rates, highlighting robustness and scalability.

Conclusions:

• The hybrid FPA-COA-ANN model effectively mitigates limitations of traditional IDS, offering a robust and efficient solution for real-time threat detection.
• The model's high accuracy and adaptability across diverse datasets underscore its potential for enhancing cybersecurity defenses.
• The proposed approach provides a scalable solution for real-time network threat detection in dynamic and complex environments.